As the internet becomes more popular and widespread, and internet systems provide more and more options for users, the contents of websites become more complicated. Take the e-commerce website as an example, users often get lost in the variety of product information provided by the e-commerce website, and have a hard time finding the product that they need. In order to solve this problem, internet systems often, based on the user's product evaluation information, perform a recommendation algorithm to determine the products that the user would need, and then send such product information to the user. This would help the user find the product in need, and complete the purchase process.
Among the recommendation algorithms used in the internet, the singular value decomposition (SVD) is a relatively accurate algorithm, which breaks down and analyzes the main variables using a matrix to obtain dimension reduction and find latent variables. Specifically, the order m×n real matrix A may be decomposed into m-order orthogonal matrix U, n-order orthogonal matrix V, and the product of diagonal matrix S. Through the SVD algorithm, user preference information and evaluated product information may be derived from the user's product evaluation information. The product may be recommended to the user based on the user preference information and the evaluated product information.
When the current internet systems use the SVD algorithm to recommend products to users, however, they are unable to distribute the user information, product information and the user's product evaluation information to multiple calculation nodes to perform calculation. Instead, they can only perform calculation on a single calculation node. This leads to problems such as the large volume of data to be processed, the complicated calculation process, and the long consumption time. In addition, using one calculation node has a limited expansion capability. As the user information and product information increase, this processing model cannot satisfy the performance requirements.